


'''simple docstring'''
import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME





def    a__			(   lowerCAmelCase__		)       ->      Any:
    UpperCAmelCase__     :						str						=							[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''encoder.embed_positions._float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase__    ,   lowerCAmelCase__		)





def    a__			(   lowerCAmelCase__		)       ->      Optional[Any]:
    UpperCAmelCase__		,		UpperCAmelCase__     :						List[Any]						=							emb.weight.shape
    UpperCAmelCase__     :						Dict						=							nn.Linear(lowerCAmelCase__    ,   lowerCAmelCase__    ,   bias=lowerCAmelCase__		)
    UpperCAmelCase__     :						List[Any]						=							emb.weight.data
    return lin_layer





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=None		)       ->      str:
    UpperCAmelCase__     :						Union[str, Any]						=							{}
    for old_key in state_dict.keys():
        UpperCAmelCase__     :						Optional[int]						=							old_key
        if "moe_layer.experts." in key:
            if expert_idx is not None:
                UpperCAmelCase__     :						int						=							key.replace('''moe_layer.experts.0'''    ,   F"""ffn.experts.expert_{expert_idx}"""		)
            else:
                UpperCAmelCase__     :						int						=							key.replace('''moe_layer.experts.'''    ,   '''ffn.experts.expert_'''		)
        if "gate" in key:
            UpperCAmelCase__     :						Tuple						=							key.replace('''.moe_layer.gate.wg'''    ,   '''.ffn.router.classifier'''		)
        if "fc2" and "experts" not in key:
            UpperCAmelCase__     :						List[Any]						=							key.replace('''.fc2.'''    ,   '''.ffn.fc2.'''		)
        if "fc1" and "experts" not in key:
            UpperCAmelCase__     :						Tuple						=							key.replace('''.fc1.'''    ,   '''.ffn.fc1.'''		)
        if ".encoder_attn." in key:
            UpperCAmelCase__     :						Union[str, Any]						=							key.replace('''.encoder_attn.'''    ,   '''.cross_attention.'''		)
        if "encoder_attn_layer_norm" in key:
            UpperCAmelCase__     :						List[Any]						=							key.replace('''encoder_attn_layer_norm'''    ,   '''cross_attention_layer_norm'''		)
        if "final_layer_norm" in key:
            UpperCAmelCase__     :						int						=							key.replace('''final_layer_norm'''    ,   '''ff_layer_norm'''		)
        UpperCAmelCase__     :						Tuple						=							state_dict[old_key]
    return new_dict





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__ = WEIGHTS_NAME		)       ->      int:
    UpperCAmelCase__     :						List[Any]						=							[]
    UpperCAmelCase__     :						Tuple						=							0
    os.makedirs(lowerCAmelCase__    ,   exist_ok=lowerCAmelCase__		)

    for expert in range(lowerCAmelCase__		):
        UpperCAmelCase__     :						Tuple						=							switch_checkpoint_path + F"""-rank-{expert}.pt"""
        if os.path.isfile(lowerCAmelCase__		):
            UpperCAmelCase__     :						int						=							torch.load(lowerCAmelCase__		)['''model''']
            remove_ignore_keys_(lowerCAmelCase__		)
            UpperCAmelCase__     :						str						=							rename_fairseq_keys(lowerCAmelCase__    ,   lowerCAmelCase__		)
            UpperCAmelCase__     :						Optional[Any]						=							os.path.join(
                lowerCAmelCase__    ,   weights_name.replace('''.bin'''    ,   F"""-{len(lowerCAmelCase__		)+1:05d}-of-???.bin"""		)		)
            torch.save(lowerCAmelCase__    ,   lowerCAmelCase__		)
            sharded_state_dicts.append(expert_state.keys()		)
            total_size += sum([value.numel() for key, value in expert_state.items()]		) * dtype_byte_size(
                expert_state[list(lowerCAmelCase__		)[0]].dtype		)

    # Add the last block
    UpperCAmelCase__     :						Union[str, Any]						=							os.path.join(lowerCAmelCase__    ,   weights_name.replace('''.bin'''    ,   F"""-{len(lowerCAmelCase__		)+1:05d}-of-???.bin"""		)		)
    UpperCAmelCase__     :						Optional[int]						=							torch.load(switch_checkpoint_path + '''-shared.pt'''		)['''model''']
    remove_ignore_keys_(lowerCAmelCase__		)
    UpperCAmelCase__     :						Dict						=							rename_fairseq_keys(lowerCAmelCase__    ,   lowerCAmelCase__		)
    UpperCAmelCase__     :						str						=							shared_weights['''decoder.embed_tokens.weight''']
    sharded_state_dicts.append(shared_weights.keys()		)

    # If we only have the shared weights (dummy model/experts saved on the same file)
    if len(lowerCAmelCase__		) == 1:
        UpperCAmelCase__     :						Union[str, Any]						=							os.path.join(lowerCAmelCase__    ,   lowerCAmelCase__		)
        torch.save(lowerCAmelCase__    ,   lowerCAmelCase__		)
        return {weights_name: sharded_state_dicts[0]}, None
    else:
        torch.save(lowerCAmelCase__    ,   lowerCAmelCase__		)
    # Otherwise, let's build the index
    UpperCAmelCase__     :						int						=							{}
    for idx, shard in enumerate(lowerCAmelCase__		):
        UpperCAmelCase__     :						List[str]						=							weights_name.replace('''.bin'''    ,   F"""-{idx+1:05d}-of-{len(lowerCAmelCase__		):05d}.bin"""		)
        UpperCAmelCase__     :						Tuple						=							os.path.join(lowerCAmelCase__    ,   weights_name.replace('''.bin'''    ,   F"""-{idx+1:05d}-of-???.bin"""		)		)
        os.rename(lowerCAmelCase__    ,   os.path.join(lowerCAmelCase__    ,   lowerCAmelCase__		)		)
        for key in shard:
            UpperCAmelCase__     :						Optional[int]						=							shard_file

    # Add the metadata
    UpperCAmelCase__     :						str						=							{'''total_size''': total_size}
    UpperCAmelCase__     :						Optional[int]						=							{'''metadata''': metadata, '''weight_map''': weight_map}

    with open(os.path.join(lowerCAmelCase__    ,   lowerCAmelCase__		)    ,   '''w'''    ,   encoding='''utf-8'''		) as f:
        UpperCAmelCase__     :						Optional[int]						=							json.dumps(lowerCAmelCase__    ,   indent=2    ,   sort_keys=lowerCAmelCase__		) + '''\n'''
        f.write(lowerCAmelCase__		)

    return metadata, index


if __name__ == "__main__":
  UpperCamelCase__          = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--nllb_moe_checkpoint_path''',
      default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
      type=str,
      required=False,
      help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
  )
  parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
      type=str,
      required=False,
      help='''Path to the output pytorch model.''',
  )
  UpperCamelCase__          = parser.parse_args()
  UpperCamelCase__   ,  UpperCamelCase__          = shard_on_the_fly(
      args.nllb_moe_checkpoint_path,
      args.pytorch_dump_folder_path,
      1_2_8,
      args.dtype,
  )

  UpperCamelCase__          = NllbMoeConfig.from_pretrained(
      '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
  )
  config.save_pretrained(args.pytorch_dump_folder_path)
  UpperCamelCase__          = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
  print('''Done''')
  model.save_pretrained(args.pytorch_dump_folder_path)








'''simple docstring'''







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def       __UpperCamelCase					(      *lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       if not isinstance(lowercase_   ,       lowercase_   ):
              a_						     =			list(lowercase_   )
       for i in range(len(lowercase_   )   ):
              a_						     =			None
       gc.collect()
       if is_xpu_available():
              torch.xpu.empty_cache()
       elif is_npu_available():
              torch.npu.empty_cache()
       else:
              torch.cuda.empty_cache()
       return objects


def       __UpperCamelCase					(      lowercase_       :  Exception   ):
       """simple docstring"""




       a_						     =			[
           'CUDA out of memory.',  # CUDA OOM
           'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.',  # CUDNN SNAFU
           'DefaultCPUAllocator: can\'t allocate memory',  # CPU OOM
       ]
       if isinstance(lowercase_   ,       lowercase_   ) and len(exception.args   ) == 1:
              return any(err in exception.args[0] for err in _statements   )
       return False


def       __UpperCamelCase					(      lowercase_       :  callable = None   ,       lowercase_       :  int = 128   ):
       """simple docstring"""




       if function is None:
              return functools.partial(lowercase_   ,       starting_batch_size=lowercase_   )

       a_						     =			starting_batch_size

       def decorator(*lowercase_       :  str   ,       **lowercase_       :  Dict   ):
              nonlocal batch_size
              gc.collect()
              if is_xpu_available():
                     torch.xpu.empty_cache()
              elif is_npu_available():
                     torch.npu.empty_cache()
              else:
                     torch.cuda.empty_cache()
              a_						     =			list(inspect.signature(lowercase_   ).parameters.keys()   )
              # Guard against user error
              if len(lowercase_   ) < (len(lowercase_   ) + 1):
                     a_						     =			', '.join([F'{arg}={value}' for arg, value in zip(params[1:]   ,       args[1:]   )]   )
                     raise TypeError(
                         F'Batch size was passed into `{function.__name__}` as the first argument when called.'
                         F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'   )
              while True:
                     if batch_size == 0:
                            raise RuntimeError('No executable batch size found, reached zero.'   )
                     try:
                            return function(lowercase_   ,       *lowercase_   ,       **lowercase_   )
                     except Exception as e:
                            if should_reduce_batch_size(lowercase_   ):
                                   gc.collect()
                                   if is_xpu_available():
                                          torch.xpu.empty_cache()
                                   elif is_npu_available():
                                          torch.npu.empty_cache()
                                   else:
                                          torch.cuda.empty_cache()
                                   batch_size //= 2
                            else:
                                   raise

       return decorator




